Metadata Blending: Automating Data Lineage Views

November 6, 2018

Business Intelligence (BI) cannot fulfill its namesake (i.e. intelligence) without validated information about the data used for reports and visualizations. Simply pushing data of any kind and any origin into a dashboard yields attractive graphs of no particular value to decision makers and analysts. Without trust in the data, which comes from knowledge and validation of all of its characteristics, it is just as meaningful to make business graphs from weekly winning lottery numbers. The intelligence arises from gleaning actionable insights.

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Atlantbh

The Leading Software Development and Outsourcing Company in Bosnia and Herzegovina, with years of experience in Outsourcing, Distributed Agile and Lean, GIS and Mapping, Big Data, Enterprise Software, Cloud, Web and Mobile, working with the most respectable companies worldwide.

OTHER WHITEPAPERS
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Building Data Products: The Ultimate How-To Guide

whitePaper | November 18, 2022

It’s time to treat data like a product, not an afterthought. A new paradigm has emerged among forward-thinking data teams: treat your data like a product. This may sound easy in theory, but it’s far from it! Product development processes have become incredibly sophisticated, with resources dedicated to reliability, adoption, feature roadmaps, and more.

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Implementing an Operational Data Layer for Product Catalog Modernization

whitePaper | September 27, 2022

The product catalog is at the heart of any retail company- simply put it is the window into the list of all products that are for sale and their information. These days that information can be extensive- from name, dimensions, price (and per-store price) to images, videos, and third party reviews.

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O'Reilly Data Quality Fundamentals

whitePaper | November 18, 2022

For decades, the lack of visibility into the health of our data has led to data downtime in companies around the world. When data is missing, inaccurate, or erroneous, data quality initiatives are doomed to fail, leaving enterprises at a privacy compliance and business disadvantage.

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DataOS®: A Paradigm Shift in Data Management – Creating Scalable Analytics

whitePaper | October 10, 2022

Companies undergoing digital transformation must make data available to all stakeholders. However, outdated security and governance tools can prevent companies from freeing their data without opening themselves up to new risks.

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The Evolution of Clinical Data Management into Clinical Data Science

whitePaper | September 12, 2022

Clinical Data Management (CDM) evolved over the last two decades from managing data entered on paper Case Report Forms (CRFs) to managing data transcribed into Electronic Data Capture (EDC) systems. The Society for Clinical Data Management (SCDM) strongly contributed to this first significant CDM evolution through its Good Clinical Data Management Practice 1 (GCDMP©) Chapters first published in 2000 and its certification program for Clinical Data Managers2 subsequently released in 2004.

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A comprehensive guide to implementing Master Data Management with the Microsoft Intelligent Data Platform

whitePaper | December 28, 2022

For companies looking to innovate, minimize costs, and monetize new opportunities, how they drive value from their data estate is a determining factor in whether they succeed or fail. Similar to the speed, scalability, and efficiencies offered by Cloud computing, the ability for companies to do the same with their data operations – although not yet realized for most – will have a similar impact.

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Atlantbh

The Leading Software Development and Outsourcing Company in Bosnia and Herzegovina, with years of experience in Outsourcing, Distributed Agile and Lean, GIS and Mapping, Big Data, Enterprise Software, Cloud, Web and Mobile, working with the most respectable companies worldwide.

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